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The software implements probabilistic modeling of astronomical images of stars and galaxies as seen by multiple telescopes and/or at multiple observation times. The image models are rendered with the external software package GalSim (https://github.com GalSim- developers GalSim ). JIF primarily handles the parsing of image model parameters, Monte Carlo sampling, and diagnostic plots.
Wind energy researchers typically share one key characteristic: a passion for increasing wind energy in the global energy mix. The U.S. Department of Energy (DOE) supports this mission in a number of ways including allocating funding directly to various aspects of wind energy research through the Office of Energy Efficiency and Renewable Energy (EERE) via the Wind Energy Technologies Office (WETO). While the traditional output of research is academic publication, software development efforts are increasingly a major focus. Software tools in the research environment allow researchers to describe an idea and quickly increase the scope and scale as they study it further. As a product of research, these tools represent a direct pipeline from researcher to industry practitioners since they are the implementation of ideas described in academic publications. Given this vital role in wind energy research and commercial development, the broad research software portfolio supported by WETO must maintain a minimum level of quality to support the wind energy field in the growing transition to renewable energy. This report outlines a series o f best practices to be adopted by all WETO-supported software projects, as well as expectations that the communities interacting with these projects should have of the developers and tools themselves. Wind energy research software has a unique standing in the field of scientific software. The stakeholders are varied with a subset being: (1) DOE EERE leadership, (2) DOE WETO leadership and program managers, (3) National lab leadership, (4) Associated project principle investigators, (5) Research software engineers, (6) Wind energy researchers in academia (including graduate students, post docs, and national lab staff), (7) Industry researchers and practitioners, (8) Commercial software developers, and (9) The general public interested in wind energy. These software are typically the end-user of other generic software libraries, so the funding cycles are often tied to applied research rather than the development of the software itself. Since the developers are also wind energy researchers, these tools are typically designed in a way that closely resembles the application in which they're used. Additionally, the expertise and incentives for the developers have a high variability, and often neither are aligned with software engineering or computer science. Given the unique environment in which wind energy research software is produced and consumed, it is critical for model owners to understand the context of their software. A framework for developing this understanding is to answer the following questions of a given software project: What is it's purpose? What is its role in the field of wind energy? What is the profile of the expected users? For how long will it be relevant? What is the expected impact? These questions allow model owners to identify the appropriate methods for the design, development, and long term maintenance of their software. Additionally, the answer provide context for future planners to understand why particular decisions were made and discern the consequences of changing course. The information is aggregated from experience within WETO-supported software development groups as well as external organizations and efforts to define the craft of research software engineering. These best practices aim to make the collaborative development process efficient and effective while improving the model understanding across stakeholders. Additionally, the general adoption of a common framework for software quality ensures that the end users of WETO software can trust these tools and accurately understand the risks to workflow integration.
Neuromorphic systems can be realized by a variety of algorithms and architectures. A common understanding is that spiking neuromorphic designs, which encode information into spatio-temporal spiking events, are both a biologically-accurate and efficient way of processing information. However, representing the information through timing relationships induces sophisticated circuit designs in traditional CMOS-based implementations. In recent years, high-capacity resistive memory (RRAM, aka, memristor) has demonstrated great potential in mimicking synaptic behaviors. Several RRAM-based spiking neuromorphic designs exist, most of which focus on rate coding schemes. These designs simplify circuit implementations of neuron models and explore challenges such as unsatisfactory speed, resolution, and performance. As an alternative, we will explore temporal coding spiking neuromorphic systems that encode information as the relative timing of neuron activations (spikes), which have been proven to be more adaptive and energy-efficient. Developing a neuromorphic system for spiking neural network (SNN) inference and online training, however, faces some major technical challenges: (1) It lacks circuit implementation support for temporal-coding SNN to achieve satisfying power efficiency and accuracy; (2) Although existing research works have investigated memristive synapse and neuron designs for spike-timing-dependent plasticity, the non-ideal conditions in implementation, such as device variations and signal degradation, degrade online learning accuracy of large scale systems; and (3) Non-optimized, inter-layer data traffic in SNNs, leads to unnecessary data communication costs. In this project, we plan to address these challenges by a hardware and software co-design framework that incorporates solutions at the circuit, architecture, and algorithm levels. At the circuit-level, we will elaborate on the in-situ SNN processing element designs for supporting both inference and online training modes. Variation-aware schemes will be studied to improve reliability. At the architecture level, we propose a pipelined, asynchronous architecture to retain the timing resolution of spikes. At the algorithm level, we will investigate an innovative SNN training algorithm for enabling activation sparsification and reducing unnecessary data communication costs. This neuromorphic system will provide an effective solution to real-life energy-constrained applications and significantly contribute to the exploration of next-generation high-performance computing systems under the DOE context.
Cybersecurity vulnerability assessment tools, frameworks, and methodologies are used to understand the cybersecurity maturity of a system or a facility. However, these tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy; the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we have been developing a framework and software application called cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). CyFEr treats the problem at hand as a multi-criteria decision analysis (MCDA) problem, which requires that various criteria be weighed relatively. Defining those weights is non-trivial and often leads to subjective decisions leading to undesired complications. To facilitate such a weighting system in CyFEr, we evaluated the application of various rank-weight methods (such as rank sum, reciprocal rank, rank exponent, and rank order centroid). The efficacy of those rank-weight methods was evaluated by applying them and testing against the blockchain cybersecurity framework (BC2F). BC2F was developed using the NIST cybersecurity framework to evaluate the cybersecurity posture of the blockchain nodes and networks in a given blockchain application or use-case. This paper provides 1) technical insights on the application of rank-weight methods to cybersecurity vulnerability assessments, 2) an overview of BC2F, 3) the application of rank-weight methods to BC2F, and 4) a depiction of the integration of the discussed rank-weight methods in CyFEr.
CALPHAD uncertainty quantification (UQ) is the foundation of materials design with quantified confidence. We report a framework and software packages to enable CALPHAD UQ assessment and calculation using commercial CALPHAD software (Thermo-Calc). This Bayesian inference framework is coupled with a Markov chain Monte Carlo algorithm to establish uncertainty traces with a given thermodynamic database file (TDB) and corresponding experimental data points. This general framework is demonstrated with the Ni–Cr binary system. The algorithm is firstly validated on synthetic data with known ground truth. Then it is applied to real experimental data to generate posterior traces. We develop a file format named TDBX, which provides a single source of truth by combining the original TDB content and the traces for each assessed Gibbs energy parameter. CALPHAD UQ calculations are performed based on the TDBX file, from which uncertainties for phase boundaries, enthalpy curves, and solidification range are collected as examples of basic design parameters. This TDBX file with corresponding scripts are made open-source. Finally, the combination of CALPHAD UQ assessments and calculations connected by TDBX supports uncertainty-assisted modeling, enabling the integrated application of modern design with uncertainty methodologies to computational materials design.
Existing cybersecurity vulnerability assessment tools were designed based on the policies and standards defined by organizations such as the U.S. Department of Energy and the National Institute of Standards and Technology (NIST). Frameworks such as the cybersecurity capability maturity model (C2M2) and the NIST Cybersecurity Framework (CSF) are often used by the critical infrastructure owners and operators to determine the cybersecurity maturity of their facility. Although these frameworks are exceptional at performing qualitative cybersecurity analysis and identifying vulnerabilities, they do not provide a means to perform prioritized mitigation of those vulnerabilities in order to achieve a desired cybersecurity maturity. To address that challenge, we developed a framework and software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). This paper presents the detailed architecture of CyFEr’s enhanced prioritized gap analysis (EPGA) methodology and its application to CSF. The efficacy of the presented framework is demonstrated by comparing against existing similar models and testing against the cyber injects from a real-world cyber-attack that targeted industrial control systems (ICS) in critical infrastructures.
Here, we propose an intelligent framework for Traffic Matrix (TM) inference in Software Defined Networks (SDN) where the Ternary Content Addressable Memory (TCAM) entries of switches are partitioned into two parts to: (1) effectively aggregate part of incoming flows for aggregate measurements, and (2) de-aggregate and directly measure the most informative flows for per-flow measurements. These measurements are then processed to effectively estimate the size of network flows. Under hard resource constraints of limited TCAM sizes, we show how to design the optimal and efficient-compressed flow aggregation matrices. We propose an optimal Multi-Armed Bandit (MAB) based algorithm to adaptively measure the most rewarding flows. We evaluate the performance of our framework using real traffic traces from different network environments and by considering two main applications: TM estimation and Heavy Hitter (HH) detection. Moreover, we have implemented a prototype of our framework in Mininet to demonstrate its effectiveness.
The software provides a framework to build a fast look up layer that reflects the holdings of an archive or database and operates between search/discovery system and disk storage. The software utilizes Bloom filter (BF) data structure. The discovery service powered by the Bloom filter layer comes with a very high level of accuracy yet space-saving, since BF compresses data to fit into RAM.
Abstract not provided.
The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.
Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.
This report is a continuation of the series of Hunter Dose Tracking System (HDTS) Quality Assurance documents including (Foley and Powell, 2010; Dixon, 2012). In this report we have created a suite of automated test cases and a system to analyze the results of those tests as well as documented the methodology to ensure the field system performs within specifications. The software test cases cover all of the functions and interactions of functions that are practical to test. With the developed framework, if software defects are discovered, it will be easy to create one or more test cases to reproduce the defect and ensure that code changes correct the defect. These tests con rm HDTS version 2017.0 performs according to its specifications and documentation and that its performance meets the needs of its users at the Savannah River Site.
This report is a continuation of the series of Hunter Dose Tracking System (HDTS) Quality Assurance documents including (Foley and Powell, 2010; Dixon, 2012; Whiteside, 2017b). In this report we have created a suite of automated test cases and a system to analyze the results of those tests as well as documented the methodology to ensure the field system performs within specifications. The software test cases cover all of the functions and interactions of functions that are practical to test. With the developed framework, if software defects are discovered, it will be easy to create one or more test cases to reproduce the defect and ensure that code changes correct the defect.
For decades, Lawrence Livermore National Laboratory (LLNL) has been engaged in significant research, development, and support for software to enable scientific computing and, particularly, the use of high performance computing (HPC) in the NNSA mission space. In particular, the move in the mid-1990’s to simulation as a leading component of stockpile stewardship through the ASCI and the successor ASC programs, as well as the need for reliable data acquisition and control software for the National Ignition Facility, have been important drivers in building expertise in production-quality software development at LLNL. LLNL has also been a leader in the DOE SciDAC FASTMath Institute and the DOE Exascale Computing Project (ECP), both of which have striven to make scientific computing software – in particular, the enabling technologies underpinning simulation capabilities – more widely adopted and sustainable. As such, we believe that our experience can inform the broader goal of software stewardship for scientific and high-performance computing. LLNL strongly supports the formation of a new DOE ASCR program element in software stewardship and sustainment. Historically, DOE ASCR has funded applied mathematics and computer science research that has led to the development of important new capabilities and algorithms that are expressed as artifacts in research software. Such frameworks, libraries, and tools have seldom been directly funded to address the important issues of code maintenance, documentation, robustness, and community building. Software engineering and support have typically been done on the side in support of the ASCR-driven research products. DOE funding priorities have been slow to recognize that good software engineering, the kind that ensures research investments have more adoption and longevity, requires significant resources. Based upon our experiences, we have prepared this response to highlight the concerns and issues we believe to be important as DOE ASCR considers its role in scientific software stewardship. We believe that role is important and will require a significant investment of new funding to legitimately support the technologies past and future DOE ASCR investments have and will produce to facilitate their uptake and adoption in the broader scientific computing community. Following a summary of our involvement in scientific software development, the remainder our response is organized around the nine topics specifically identified in the RFI.
With the dramatic increase in scale expected for Exascale computing, there is a dire need for tuning of hardware configurations and software optimizations such that they are in unison. However, the expected increase in tunable hardware parameters makes searching through the design space for optimal hardware-and-software configurations much more challenging. Towards this end, we propose a composable hardware-software optimization framework called Prometheus. Prometheus uses a combination of analytical and machine-learning techniques to capture application characteristics and subsequently determine the hardware-software configuration for near-optimal performance. We evaluate Prometheus for its efficacy using four widely used proxy applications: LULESH, CoMD, CG and CoEVP. We demonstrate that Prometheus identifies near-optimal hardwaresoftware configurations and verify the results via brute-force search of the design space.
Spiking, neuromorphic computing systems are in a period of active exploration by the computing community. While they feature computational expressiveness beyond both von Neumann computing models and feed-forward neural networks, they are also challenging to design and program. The TENNLab exploratory neuromorphic computing framework is a software infrastructure, soon to be open-source, whose goal is to enable potential users of spiking, neuromorphic computing systems to develop applications and evaluate computing architectures, and for architecture researchers to develop and evaluate their architectures with a variety of applications. In this letter, we present the software architecture of the TENNLab framework.
Communication switches have sometimes been augmented to process collectives (e.g., the IBM BlueGene project and the Mellanox SHArP switch). In this work, we find that there is a great acceleration opportunity through the further augmentation of switches to accelerate more complex functions that combine communication with computation. We consider three types of such functions. The first is fully-fused collectives built by fusing multiple existing collectives like Allreduce with Alltoall. The second is semi-fused collectives built by combining a collective with another computation. The third we refer to as higher-order collectives built by combining multiple computations and communications, such as to perform matrix-matrix multiply (PGEMM). In this work, we propose a framework called SmartFuse to accelerate fused collective functions. The core of SmartFuse is a reconfigurable smart switch to support these operations. The semi/fully fused collectives are implemented with a CGRAlike architecture, while higher-order collectives are implemented with a more specialized computational unit that can also schedule communication. Supporting our framework is software to evaluate and translate relevant parts of the input program, compile them into a control data flow graph, and then map this graph to the switch hardware. The proposed framework, once deployed, has the strong potential to accelerate existing HPC applications transparently by encapsulation within an MPI implementation. Experimental results show that this approach improves the performance of the PGEMM kernel, MINIFE, and AMG by, on average, 94%, 15%, and 13%, respectively.
The RELAP-7 code is the next generation nuclear reactor system safety analysis code being developed at the Idaho National Laboratory (INL). The code is based on the INL's modern scientific software development framework, MOOSE (Multi-Physics Object Oriented Simulation Environment). The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical integration methods, and physical models. The end result will be a reactor systems analysis capability that retains and improves upon RELAP5's capability and extends the analysis capability for all reactor system simulation scenarios. RELAP-7 utilizes a single phase and a novel seven-equation two-phase flow models as described in the RELAP-7 Theory Manual (INL/EXT-14-31366). The basic equation systems are hyperbolic, which generally require some type of stabilization (or artificial viscosity) to capture nonlinear discontinuities and to suppress advection-caused oscillations. This report documents one of the available options for this stabilization in RELAP-7 -- a new and novel approach known as the entropy viscosity method. Because the code is an ongoing development effort in which the physical sub models, numerics, and coding are evolving, so too must the specific details of the entropy viscosity stabilization method. Here the fundamentals of the method in their current state are presented.